Overview

Brought to you by YData

Dataset statistics

Number of variables10
Number of observations32434489
Missing cells2078068
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 GiB
Average record size in memory80.0 B

Variable types

Numeric8
Categorical2

Alerts

eval_set has constant value "prior" Constant
days_since_prior_order has 2078068 (6.4%) missing values Missing
order_dow has 6209666 (19.1%) zeros Zeros
days_since_prior_order has 448698 (1.4%) zeros Zeros

Reproduction

Analysis started2024-11-01 20:57:22.573259
Analysis finished2024-11-01 21:07:29.668437
Duration10 minutes and 7.1 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

order_id
Real number (ℝ)

Distinct3214874
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1710748.5
Minimum2
Maximum3421083
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size247.5 MiB
2024-11-01T17:07:29.876794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile170925
Q1855943
median1711048
Q32565514
95-th percentile3250209
Maximum3421083
Range3421081
Interquartile range (IQR)1709571

Descriptive statistics

Standard deviation987300.7
Coefficient of variation (CV)0.57711621
Kurtosis-1.1991283
Mean1710748.5
Median Absolute Deviation (MAD)854783
Skewness-0.00048974146
Sum5.5487254 × 1013
Variance9.7476267 × 1011
MonotonicityIncreasing
2024-11-01T17:07:30.035708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1564244 145
 
< 0.1%
790903 137
 
< 0.1%
61355 127
 
< 0.1%
2970392 121
 
< 0.1%
2069920 116
 
< 0.1%
3308010 115
 
< 0.1%
2753324 114
 
< 0.1%
2499774 112
 
< 0.1%
2621625 109
 
< 0.1%
77151 109
 
< 0.1%
Other values (3214864) 32433284
> 99.9%
ValueCountFrequency (%)
2 9
 
< 0.1%
3 8
 
< 0.1%
4 13
< 0.1%
5 26
< 0.1%
6 3
 
< 0.1%
7 2
 
< 0.1%
8 1
 
< 0.1%
9 15
< 0.1%
10 15
< 0.1%
11 5
 
< 0.1%
ValueCountFrequency (%)
3421083 10
< 0.1%
3421082 7
< 0.1%
3421081 7
< 0.1%
3421080 9
< 0.1%
3421079 1
 
< 0.1%
3421078 9
< 0.1%
3421077 4
 
< 0.1%
3421076 8
< 0.1%
3421075 8
< 0.1%
3421074 4
 
< 0.1%

product_id
Real number (ℝ)

Distinct49677
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25576.338
Minimum1
Maximum49688
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size247.5 MiB
2024-11-01T17:07:30.196088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3376
Q113530
median25256
Q337935
95-th percentile47559
Maximum49688
Range49687
Interquartile range (IQR)24405

Descriptive statistics

Standard deviation14096.689
Coefficient of variation (CV)0.55116136
Kurtosis-1.1408165
Mean25576.338
Median Absolute Deviation (MAD)12080
Skewness-0.021130583
Sum8.2955544 × 1011
Variance1.9871664 × 108
MonotonicityNot monotonic
2024-11-01T17:07:30.348342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24852 472565
 
1.5%
13176 379450
 
1.2%
21137 264683
 
0.8%
21903 241921
 
0.7%
47209 213584
 
0.7%
47766 176815
 
0.5%
47626 152657
 
0.5%
16797 142951
 
0.4%
26209 140627
 
0.4%
27845 137905
 
0.4%
Other values (49667) 30111331
92.8%
ValueCountFrequency (%)
1 1852
< 0.1%
2 90
 
< 0.1%
3 277
 
< 0.1%
4 329
 
< 0.1%
5 15
 
< 0.1%
6 8
 
< 0.1%
7 30
 
< 0.1%
8 165
 
< 0.1%
9 156
 
< 0.1%
10 2572
< 0.1%
ValueCountFrequency (%)
49688 89
 
< 0.1%
49687 13
 
< 0.1%
49686 120
 
< 0.1%
49685 49
 
< 0.1%
49684 9
 
< 0.1%
49683 97315
0.3%
49682 108
 
< 0.1%
49681 70
 
< 0.1%
49680 1018
 
< 0.1%
49679 132
 
< 0.1%

add_to_cart_order
Real number (ℝ)

Distinct145
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.3510755
Minimum1
Maximum145
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size247.5 MiB
2024-11-01T17:07:30.489693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q311
95-th percentile22
Maximum145
Range144
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.1266712
Coefficient of variation (CV)0.85338363
Kurtosis5.643873
Mean8.3510755
Median Absolute Deviation (MAD)4
Skewness1.8180712
Sum2.7086287 × 108
Variance50.789442
MonotonicityNot monotonic
2024-11-01T17:07:30.634281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3214874
 
9.9%
2 3058126
 
9.4%
3 2871133
 
8.9%
4 2664106
 
8.2%
5 2442025
 
7.5%
6 2213695
 
6.8%
7 1986020
 
6.1%
8 1766014
 
5.4%
9 1562640
 
4.8%
10 1378293
 
4.2%
Other values (135) 9277563
28.6%
ValueCountFrequency (%)
1 3214874
9.9%
2 3058126
9.4%
3 2871133
8.9%
4 2664106
8.2%
5 2442025
7.5%
6 2213695
6.8%
7 1986020
6.1%
8 1766014
5.4%
9 1562640
4.8%
10 1378293
4.2%
ValueCountFrequency (%)
145 1
< 0.1%
144 1
< 0.1%
143 1
< 0.1%
142 1
< 0.1%
141 1
< 0.1%
140 1
< 0.1%
139 1
< 0.1%
138 1
< 0.1%
137 2
< 0.1%
136 2
< 0.1%

reordered
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size247.5 MiB
1
19126536 
0
13307953 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32434489
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 19126536
59.0%
0 13307953
41.0%

Length

2024-11-01T17:07:30.750411image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-01T17:07:30.868666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 19126536
59.0%
0 13307953
41.0%

Most occurring characters

ValueCountFrequency (%)
1 19126536
59.0%
0 13307953
41.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32434489
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 19126536
59.0%
0 13307953
41.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32434489
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 19126536
59.0%
0 13307953
41.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32434489
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 19126536
59.0%
0 13307953
41.0%

user_id
Real number (ℝ)

Distinct206209
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102937.24
Minimum1
Maximum206209
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size247.5 MiB
2024-11-01T17:07:30.998920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10469
Q151421
median102611
Q3154391
95-th percentile195736
Maximum206209
Range206208
Interquartile range (IQR)102970

Descriptive statistics

Standard deviation59466.478
Coefficient of variation (CV)0.57769645
Kurtosis-1.2009235
Mean102937.24
Median Absolute Deviation (MAD)51493
Skewness0.0066119537
Sum3.3387168 × 1012
Variance3.536262 × 109
MonotonicityNot monotonic
2024-11-01T17:07:31.124237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201268 3725
 
< 0.1%
129928 3638
 
< 0.1%
164055 3061
 
< 0.1%
186704 2936
 
< 0.1%
176478 2921
 
< 0.1%
182401 2907
 
< 0.1%
137629 2901
 
< 0.1%
33731 2888
 
< 0.1%
108187 2760
 
< 0.1%
4694 2735
 
< 0.1%
Other values (206199) 32404017
99.9%
ValueCountFrequency (%)
1 59
 
< 0.1%
2 195
< 0.1%
3 88
< 0.1%
4 18
 
< 0.1%
5 37
 
< 0.1%
6 14
 
< 0.1%
7 206
< 0.1%
8 49
 
< 0.1%
9 76
 
< 0.1%
10 143
< 0.1%
ValueCountFrequency (%)
206209 129
 
< 0.1%
206208 677
< 0.1%
206207 223
 
< 0.1%
206206 285
< 0.1%
206205 32
 
< 0.1%
206204 54
 
< 0.1%
206203 119
 
< 0.1%
206202 198
 
< 0.1%
206201 404
< 0.1%
206200 279
< 0.1%

eval_set
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size247.5 MiB
prior
32434489 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters162172445
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowprior
2nd rowprior
3rd rowprior
4th rowprior
5th rowprior

Common Values

ValueCountFrequency (%)
prior 32434489
100.0%

Length

2024-11-01T17:07:31.246716image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-01T17:07:31.335069image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
prior 32434489
100.0%

Most occurring characters

ValueCountFrequency (%)
r 64868978
40.0%
p 32434489
20.0%
i 32434489
20.0%
o 32434489
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 162172445
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 64868978
40.0%
p 32434489
20.0%
i 32434489
20.0%
o 32434489
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 162172445
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 64868978
40.0%
p 32434489
20.0%
i 32434489
20.0%
o 32434489
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 162172445
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 64868978
40.0%
p 32434489
20.0%
i 32434489
20.0%
o 32434489
20.0%

order_number
Real number (ℝ)

Distinct99
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.14205
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size247.5 MiB
2024-11-01T17:07:31.432312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median11
Q324
95-th percentile54
Maximum99
Range98
Interquartile range (IQR)19

Descriptive statistics

Standard deviation17.53504
Coefficient of variation (CV)1.0229255
Kurtosis3.256605
Mean17.14205
Median Absolute Deviation (MAD)8
Skewness1.7568963
Sum5.5599364 × 108
Variance307.47765
MonotonicityNot monotonic
2024-11-01T17:07:31.577591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2078068
 
6.4%
3 2050731
 
6.3%
2 2048332
 
6.3%
4 1820298
 
5.6%
5 1628411
 
5.0%
6 1472238
 
4.5%
7 1333847
 
4.1%
8 1219148
 
3.8%
9 1120468
 
3.5%
10 1028704
 
3.2%
Other values (89) 16634244
51.3%
ValueCountFrequency (%)
1 2078068
6.4%
2 2048332
6.3%
3 2050731
6.3%
4 1820298
5.6%
5 1628411
5.0%
6 1472238
4.5%
7 1333847
4.1%
8 1219148
3.8%
9 1120468
3.5%
10 1028704
3.2%
ValueCountFrequency (%)
99 12436
< 0.1%
98 12858
< 0.1%
97 13367
< 0.1%
96 13746
< 0.1%
95 14528
< 0.1%
94 15212
< 0.1%
93 15407
< 0.1%
92 16229
0.1%
91 16862
0.1%
90 17209
0.1%

order_dow
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7388177
Minimum0
Maximum6
Zeros6209666
Zeros (%)19.1%
Negative0
Negative (%)0.0%
Memory size247.5 MiB
2024-11-01T17:07:31.686017image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0900491
Coefficient of variation (CV)0.76312092
Kurtosis-1.3339893
Mean2.7388177
Median Absolute Deviation (MAD)2
Skewness0.18019293
Sum88832152
Variance4.3683052
MonotonicityNot monotonic
2024-11-01T17:07:31.767599image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 6209666
19.1%
1 5665856
17.5%
6 4500304
13.9%
2 4217798
13.0%
5 4209533
13.0%
3 3844117
11.9%
4 3787215
11.7%
ValueCountFrequency (%)
0 6209666
19.1%
1 5665856
17.5%
2 4217798
13.0%
3 3844117
11.9%
4 3787215
11.7%
5 4209533
13.0%
6 4500304
13.9%
ValueCountFrequency (%)
6 4500304
13.9%
5 4209533
13.0%
4 3787215
11.7%
3 3844117
11.9%
2 4217798
13.0%
1 5665856
17.5%
0 6209666
19.1%

order_hour_of_day
Real number (ℝ)

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.424977
Minimum0
Maximum23
Zeros218948
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size247.5 MiB
2024-11-01T17:07:32.718644image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q110
median13
Q316
95-th percentile21
Maximum23
Range23
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.246365
Coefficient of variation (CV)0.31630333
Kurtosis-0.011657572
Mean13.424977
Median Absolute Deviation (MAD)3
Skewness-0.044082776
Sum4.3543228 × 108
Variance18.031616
MonotonicityNot monotonic
2024-11-01T17:07:32.832329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
10 2764426
 
8.5%
11 2738582
 
8.4%
14 2691548
 
8.3%
15 2664533
 
8.2%
13 2663292
 
8.2%
12 2620847
 
8.1%
16 2537458
 
7.8%
9 2456713
 
7.6%
17 2089465
 
6.4%
8 1719973
 
5.3%
Other values (14) 7487652
23.1%
ValueCountFrequency (%)
0 218948
 
0.7%
1 115786
 
0.4%
2 69434
 
0.2%
3 51321
 
0.2%
4 53283
 
0.2%
5 88062
 
0.3%
6 290795
 
0.9%
7 891937
 
2.7%
8 1719973
5.3%
9 2456713
7.6%
ValueCountFrequency (%)
23 402620
 
1.2%
22 634734
 
2.0%
21 796370
 
2.5%
20 977038
 
3.0%
19 1259401
3.9%
18 1637923
5.0%
17 2089465
6.4%
16 2537458
7.8%
15 2664533
8.2%
14 2691548
8.3%

days_since_prior_order
Real number (ℝ)

Missing  Zeros 

Distinct31
Distinct (%)< 0.1%
Missing2078068
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean11.104074
Minimum0
Maximum30
Zeros448698
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size247.5 MiB
2024-11-01T17:07:32.986921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median8
Q315
95-th percentile30
Maximum30
Range30
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.7789143
Coefficient of variation (CV)0.79060299
Kurtosis-0.068967377
Mean11.104074
Median Absolute Deviation (MAD)4
Skewness1.0539722
Sum3.3707995 × 108
Variance77.069337
MonotonicityNot monotonic
2024-11-01T17:07:33.102011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
7 3479504
 
10.7%
30 3070057
 
9.5%
6 2519939
 
7.8%
5 2126420
 
6.6%
4 2080560
 
6.4%
8 1933815
 
6.0%
3 1877881
 
5.8%
2 1464875
 
4.5%
9 1218973
 
3.8%
14 1030605
 
3.2%
Other values (21) 9553792
29.5%
(Missing) 2078068
 
6.4%
ValueCountFrequency (%)
0 448698
 
1.4%
1 941116
 
2.9%
2 1464875
4.5%
3 1877881
5.8%
4 2080560
6.4%
5 2126420
6.6%
6 2519939
7.8%
7 3479504
10.7%
8 1933815
6.0%
9 1218973
 
3.8%
ValueCountFrequency (%)
30 3070057
9.5%
29 175640
 
0.5%
28 253349
 
0.8%
27 204251
 
0.6%
26 177949
 
0.5%
25 180584
 
0.6%
24 193590
 
0.6%
23 226030
 
0.7%
22 309118
 
1.0%
21 444557
 
1.4%

Interactions

2024-11-01T17:05:49.987464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:02:37.887980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:03:04.936382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:03:32.869215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:03:59.968371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:04:27.119539image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:04:53.522860image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:05:20.718091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:05:54.070040image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:02:41.271560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:03:08.468747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:03:36.236341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:04:03.338308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:04:30.474188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:04:56.818204image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:05:24.451676image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:05:58.220925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:02:44.604698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:03:11.952339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:03:39.472200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:04:06.635708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:04:33.803143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:05:00.086202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:05:27.873030image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:06:02.237070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:02:47.907641image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:03:15.419078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:03:42.806352image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:04:10.369088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:04:37.140425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:05:03.333919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:05:31.533732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:06:06.588041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:02:51.169461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:03:18.920554image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:03:46.236115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:04:13.669440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:04:40.286708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:05:06.568363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:05:35.071642image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:06:10.554041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:02:54.590353image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:03:22.385459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:03:49.635108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:04:16.958490image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:04:43.548963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:05:09.770288image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:05:38.703968image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:06:14.039645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:02:57.836370image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:03:25.919094image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:03:53.069198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:04:20.252637image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:04:46.787241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:05:13.437996image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:05:42.341194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:06:17.253915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:03:01.406583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:03:29.519342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:03:56.569957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:04:23.769147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:04:50.219233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:05:17.038270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:05:46.105885image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-01T17:07:33.203167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
add_to_cart_orderdays_since_prior_orderorder_doworder_hour_of_dayorder_idorder_numberproduct_idreordereduser_id
add_to_cart_order1.0000.078-0.015-0.015-0.0000.0000.0090.0960.000
days_since_prior_order0.0781.000-0.043-0.004-0.000-0.3850.0010.1400.000
order_dow-0.015-0.0431.0000.0120.0010.015-0.0030.018-0.002
order_hour_of_day-0.015-0.0040.0121.0000.001-0.0480.0010.0380.000
order_id-0.000-0.0000.0010.0011.000-0.000-0.0000.001-0.000
order_number0.000-0.3850.015-0.048-0.0001.000-0.0020.341-0.001
product_id0.0090.001-0.0030.001-0.000-0.0021.0000.0400.000
reordered0.0960.1400.0180.0380.0010.3410.0401.0000.004
user_id0.0000.000-0.0020.000-0.000-0.0010.0000.0041.000

Missing values

2024-11-01T17:06:18.553543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-01T17:06:32.419362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

order_idproduct_idadd_to_cart_orderreordereduser_ideval_setorder_numberorder_doworder_hour_of_daydays_since_prior_order
023312011202279prior3598.0
122898521202279prior3598.0
22932730202279prior3598.0
324591841202279prior3598.0
423003550202279prior3598.0
521779461202279prior3598.0
624014171202279prior3598.0
72181981202279prior3598.0
824366890202279prior3598.0
933375411205970prior1651712.0
order_idproduct_idadd_to_cart_orderreordereduser_ideval_setorder_numberorder_doworder_hour_of_daydays_since_prior_order
32434479342108378541025247prior242621.0
324344803421083453092025247prior242621.0
324344813421083211623025247prior242621.0
324344823421083181764125247prior242621.0
324344833421083352115025247prior242621.0
324344843421083396786125247prior242621.0
324344853421083113527025247prior242621.0
32434486342108346008025247prior242621.0
324344873421083248529125247prior242621.0
324344883421083502010125247prior242621.0